Quantitating Metabolites in Protein Precipitated Serum Using NMR Spectroscopy
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- 作者：G. A. Nagana Gowda ; Daniel Raftery
- 刊名：Analytical Chemistry
- 出版年：2014
- 出版时间：June 3, 2014
- 年：2014
- 卷：86
- 期：11
- 页码：5433-5440
- 全文大小：415K
- 年卷期：v.86,no.11(June 3, 2014)
- ISSN：1520-6882
文摘
Quantitative NMR-based metabolite profiling is challenged by the deleterious effects of abundant proteins in the intact blood plasma/serum, which underscores the need for alternative approaches. Protein removal by ultrafiltration using low molecular weight cutoff filters thus represents an important step. However, protein precipitation, an alternative and simple approach for protein removal, lacks detailed quantitative assessment for use in NMR based metabolomics. In this study, we have comprehensively evaluated the performance of protein precipitation using methanol, acetonitrile, perchloric acid, and trichloroacetic acid and ultrafiltration approaches using 1D and 2D NMR, based on the identification and absolute quantitation of 44 human blood metabolites, including a few identified for the first time in the NMR spectra of human serum. We also investigated the use of a 鈥渟mart isotope tag,鈥?15N-cholamine for further resolution enhancement, which resulted in the detection of a number of additional metabolites. 1H NMR of both protein precipitated and ultrafiltered serum detected all 44 metabolites with comparable reproducibility (average CV, 3.7% for precipitation; 3.6% for filtration). However, nearly half of the quantified metabolites in ultrafiltered serum exhibited 10鈥?4% lower concentrations; specifically, tryptophan, benzoate, and 2-oxoisocaproate showed much lower concentrations compared to protein precipitated serum. These results indicate that protein precipitation using methanol offers a reliable approach for routine NMR-based metabolomics of human blood serum/plasma and should be considered as an alternative to ultrafiltration. Importantly, protein precipitation, which is commonly used by mass spectrometry (MS), promises avenues for direct comparison and correlation of metabolite data obtained from the two analytical platforms to exploit their combined strength in the metabolomics of blood.